BMC Medical Education

M.D.-Ph.D. programs in the United States have traditionally followed a "2-n-2" curricular model, with the graduate phase occurring between the pre-clerkship and clerkship portions of medical training. While well established, this format can limit trainee autonomy in shaping their physician-scientist training trajectories. In response, some programs have introduced a "3-n-1" model, allowing students to complete clerkships before beginning Ph.D. training. Our institution implemented multiple flexible curricula in 2017. Understanding why trainees choose one pathway is important as programs consider implementing more adaptable training structures. To investigate these factors, we surveyed M.D.-Ph.D. students at our institution, which offers multiple flexible curricular alternatives, about contributions to their curricular decisions. Responses supported that trainees weigh considerations across medical education, scientific development, and integrated physician-scientist training domains. Although the traditional pathway was a popular option, most students pursued one of the flexible pathways. Our findings suggest that introducing flexibility in the timing of undergraduate medical education and graduate training can support diverse educational, logistical, and personal priorities while maintaining the rigor of physician-scientist training. Offering multiple pathways empowers trainees to design trajectories that best fit their needs. Continued longitudinal studies are needed to assess the long-term impacts of flexible curricula on physician-scientist career outcomes.

Abstract Background: Artificial Intelligence (AI) is increasingly integrated into healthcare systems worldwide and medical schools worldwide have begun integrating AI into their curricula. The healthcare system in Iraq is currently undergoing development and AI has not yet been adopted in clinical practice in Erbil; in addition, no formal AI instruction has been incorporated into the medical education curriculum. The aim of this study was to assess knowledge levels, attitudes, and perceptions regarding AI among medical students and healthcare professionals in Erbil, Kurdistan Region of Iraq. Methods: A mixed-methods survey was distributed to medical students and residents in Erbil, Kurdistan Region of Iraq. The survey was adapted from Teng et al, and modified to reflect the local context. The survey was translated into Kurdish and Arabic. Convenience sampling was used. Statistical analysis was conducted using IBM SPSS (Statistical Package for Social Sciences), Version 26.0. Chi-square and Fishers exact tests were used to test associations between categorical variables. Mann Whitney U test was used to compare mean ranks between groups in the non-normally distributed data. A P value <0.05 was considered statistically significant. Thematic analysis was applied to open-ended qualitative responses by two independent reviewers. Results: A total of 368 participants participated in this study. The majority (85.6%) of participants felt that AI should be taught in schools and universities, and 90.8% reported using AI. ChatGPT was by far the most commonly used AI tool (85.3%). Participants aged 20-24 years (93.2%) and 25-29 years (90.2%) showed the highest prevalence of using AI. Participants that used AI previously, had higher scores for support for AI development in their field (U = 3744.5, P=0.001), feelings of hope towards AI in their field (U = 4406.5, P = 0.004) and thinking that students should learn the basics of AI (U = 4022.5, P = 0.03). Male participants were more likely to use AI in comparision with women (P=0.045). The most common concern regarding AI was loss of jobs (33.0%), followed by overreliance on AI (22.8%). Qualitative analysis revealed themes of guarded optimism, and concerns regarding the ethical implications of AI use in medicine. Conclusion: Medical students and physicians in Erbil are early adopters of AI in spite of any formal training. In parralel, most participants expressed dissatisfaction with their understanding of the ethical implications of AI in healthcare and emphasized the need for formal AI education in healthcare curricula. The majority of participants expressed guarded optimism regarding the future of AI in healthcare. A gender gap in AI was identified, consistent with global trends with implications for professional equity.

Introduction: Early exposure to otolaryngology (ENT) procedural skills in undergraduate medical education is limited by patient safety concerns, restricted clinical opportunities, and the cost of commercial simulators. As a result, essential ENT skills are often underrepresented in structured, hands-on curricula for medical students. Methods: We developed a low-cost, multistation ENT simulation curriculum consisting of five reproducible task trainers: ear examination and otologic procedures, mirror laryngoscopy, rigid and flexible endoscopic navigation, introductory mastoid drilling, and emergency cricothyrotomy. The curriculum was delivered as a 2-hour, faculty-led workshop during a third-year medical student otolaryngology rotation. Learners rotated through stations in small groups. Pre- and post-workshop surveys assessed self-reported anatomical familiarity, procedural confidence, and educational value using a 5-point Likert scale, with additional qualitative feedback collected. Results: All participants completed pre- and post-workshop evaluations. Learners demonstrated increased confidence across all assessed anatomical and procedural domains, including otoscopy, endoscopy, mirror laryngoscopy, mastoid drilling orientation, and cricothyroid membrane identification. Educational value ratings were high across all stations, with mean scores ranging from 4.33 to 5.00. Qualitative feedback emphasized the realism, accessibility, and benefit of hands-on practice in a low-stakes learning environment. Conclusion: This low-cost, multistation ENT simulation curriculum provides a feasible and reproducible approach for introducing foundational otolaryngology skills to medical students. The structured format and affordable models support early procedural exposure and may enhance learner preparedness prior to supervised clinical encounters, particularly in settings with limited simulation resources.

Background: Postgraduate medical residents frequently face difficulty in selecting appropriate statistical tests and preparing statistical analysis plans (SAPs) for thesis work. Existing resources often identify statistical tests without guiding implementation, reporting or software execution. Aims: To describe the development, features and content validation of ChooseMyStat, a free, open source, web based interactive tool for statistical test selection and SAP text generation in clinical research. Methods: ChooseMyStat was developed as a React based web application using an iterative, AI assisted development process under direct faculty supervision. The tool uses a branching decision algorithm covering 18 inferential statistical tests, two diagnostic accuracy measures, four agreement/reliability statistics, and four descriptive statistics scenarios. For each recommendation, it generates a SAP template paragraph, a results reporting example, step by step JASP instructions, and R code. Content validation was performed using 105 open-access original research articles from 15 broad medical specialties published in Indian journals during 2024 2025. Results: The tool covers commonly used statistical methods, including t tests, ANOVA, chi square variants, non parametric alternatives, correlation, regression (linear, logistic, ordinal), survival analysis, methods for clustered or repeated data, diagnostic accuracy measures, and agreement/reliability statistics. Among 365 statistical tests identified across 105 articles (excluding normality checking procedures), 346 (94.8%) were covered by the tool. Complete coverage of all statistical methods used was observed in 86 of 105 articles (81.9%). Conclusions: ChooseMyStat integrates statistical test selection with implementation guidance, SAP generation, reporting support and software instructions within a single interface. The tool may support postgraduate research training by improving accessibility to applied biostatistics guidance.

Microbiology education in veterinary curricula requires students to integrate complex foundational knowledge with clinical application, yet traditional lecture-based approaches often emphasize memorization over higher-order reasoning. In this study, we evaluated the impact of integrating clinically oriented, case-based instruction into a veterinary microbiology course within a Doctor of Veterinary Medicine curriculum. Using a quasi-experimental, multi-year design, student outcomes were compared before (2019, 2021) and after (2022-2025) implementation of case-based teaching while maintaining consistent course content, structure, and assessments. Introduction of clinical case examples was associated with significant and sustained improvements in student evaluations across multiple domains, including perceived relevance, critical thinking, and overall course value. Instructor-related evaluation metrics also improved. Student performance, measured by final course grades, increased following the intervention without evidence of grade inflation. These findings demonstrate that integrating clinically relevant case narratives into microbiology instruction enhances student engagement and student performance. This work highlights a practical and scalable strategy for improving microbiology education, particularly within veterinary and other health-professions curricula.

Objectives: Electronic Health Records (EHRs) impose a significant time burden on physicians, often requiring work to be completed outside of scheduled hours. While this burden is well-documented, how it evolves throughout emergency medicine (EM) residency remains poorly understood. This study aimed to quantify EHR usage patterns, analyze the composition of after-shift work, and characterize the development of EHR efficiency across EM training. Methods: We conducted a retrospective cohort study of EM residents (postgraduate year [PGY] 1-4) using 5.5 years of EHR audit log data (2020-2025) at a single academic institution. We analyzed EHR time per new patient encounter, stratified by postgraduate year, and categorized activities into domains such as documentation, chart review, and orders. EHR work was measured both during and after scheduled shifts. Results: The analysis included 144 unique residents and 167,010 new patient encounters across 15,386 shifts. Encounter-attributed EHR time per encounter decreased by 52% from PGY-1 to PGY-4 (median 19.9 to 9.6 minutes, p<0.001), despite an 86% increase in patient volume per shift (median 7 to 13 encounters). This efficiency gain was driven primarily by a 69% reduction in documentation time (9.3 to 2.9 minutes), accompanied by shorter notes. After-shift work (EHR activity after the 9-hour clinical shift) was present in 89.9-94.4% of encounters. At the shift level, combined after-shift EHR time (encounter-attributed plus tracking board) was a median of 64.2 minutes per shift for PGY-1 and 104.2 minutes for PGY-4. Shift-level tracking board activity dominated the after-shift burden and increased with training (median 40.2 to 79.0 minutes per shift from PGY-1 to PGY-4). Conclusions: EM residents achieve substantial gains in on-shift EHR efficiency, with the largest reductions observed in documentation time, accompanied by shorter notes and faster input speed. However, a persistent after-hours workload, dominated by administrative and patient flow tasks, suggests that (at least at this single institution) system-level factors--not just individual skill--may contribute to this pattern. Monitoring these objective EHR metrics may help programs identify struggling learners and evaluate the impact of interventions aimed at improving resident well-being and workflow efficiency.

Background: The quality of palliative care in non-designated cancer hospitals, where approximately 70% of deaths of patients with cancer occur, remains unevaluated. This study aimed to clarify the quality of palliative care in these hospitals by comparing patient characteristics and evaluating the quality of palliative care provided by bereaved families. Methods: A questionnaire survey was conducted among bereaved family members of patients with cancer who died in 2018 at designated and non-designated cancer hospitals (excluding palliative care units). We compared the two groups regarding patient and bereaved family characteristics, quality assessment of palliative care (including Memorial Symptom Assessment Scale [MSAS]), care satisfaction, and the presence of end-of-life discussions. Results: In total, 27,944 bereaved family members agreed to participate. The mean age at death was 73.2 ({+/-}11.9) and 79.7 ({+/-}10.9) years for designated and non-designated cancer hospitals, respectively (p < 0.001, Effect Size [ES] = 0.55). The mean MSAS total score (symptom intensity) was significantly higher for designated cancer hospitals than for non-designated cancer hospitals, even after adjusting for patient characteristics (p < 0.001, ES = 0.39). Conversely, the mean adjusted overall satisfaction was significantly higher in non-designated cancer hospitals (p < 0.001, ES = 0.21) than in designated cancer hospitals. Conclusions: Non-designated cancer hospitals had older and less symptomatic patients than designated cancer hospitals. However, there was no significant clinical difference in the quality of palliative care, as assessed by the bereaved families.

Respectful maternity care [RMC] comprises the primary components of high-quality maternal health services. Evidence on RMC levels and determinants in Ethiopia is still inadequate. This study aimed to examine the reception and its determinants among postnatal women in government hospitals in the East Wallaga Zone, West Oromia. An institution-based cross-sectional study was conducted from June to October 2025, within seven days post-delivery. A structured questionnaire based on the WHO RMC tools was used. The total RMC score proved robust reliability [Cronbachs = 0.808] and was organized using the 75th-percentile threshold. Factor analysis revealed basic RMC dimensions, while logistic regression was used to identify predictors of a promising RMC experience. This study presented that only 46.8% of postpartum mothers received adequate RMC, with significant gaps in care. The main deficiencies comprised poor provider self-introduction, failure to call women by name, and infrequent communication and consent practices. Three key RMC dimensions were identified: privacy and consent, explanation and permission, and respectful communication. Using multivariate analysis, interpersonal caring practices were robust predictors of positive RMC experiences. Explaining procedures with possible events, maintaining privacy, obtaining consent, prompt responsiveness, provider self-introduction, and calling mothers by name were significantly associated factors. Sociodemographic and maternal reproductive factors were not significantly associated after adjusting for confounders. Finally, fewer than half [46.6%] of mothers experienced adequate RMC, which indicated major gaps in woman-centered care. Improving respectful interpersonal communication, informed consent, and maintaining privacy should be prioritized to boost the quality of maternal healthcare in the study area.

Background: Higher education has been transformed by the rapid integration of generative artificial intelligence (GenAI) tools into academia. The objective of the present study was to examine how and for what purposes senior undergraduate dental students use GenAI tools in academic assignments. Methods: This cross-sectional study uses data from three written assignments submitted by two consecutive cohorts of graduating fourth-year dental students at the Faculty of Dentistry at the University of British Columbia, for a total of 120 students. The assignments focused on different subjects where students had to offer their views, including community water fluoridation. When using GenAI, students were asked to disclose whether and how such tools were used, and for what purpose. Descriptive statistics (e.g., means, frequencies, and proportions) were conducted via IBM SPSS Statistics (Version 27.0). Results: From the two cohort of students, 102 (85%) disclosed the use of GenAI tools in at least one assignment; of these, 69 (67.6%) reported using these tools in all three assignments. ChatGPT was by far the most frequently used GenAI tool, reported by 89 students (87.2%). Nine students (8.8%) did not specify which tool they had used. The majority of the students (91.2%, n = 93) reported using GenAI for proofreading or grammatical editing. About 9.8% of the students (n = 10) reported more substantive uses, such as relying on GenAI to generate in part or in full the assignment, and/or assessing the credibility of references. Conclusions: In our study, the use of GenAI tools was highly prevalent among senior undergraduate dental students for editorial purposes. A smaller but notable proportion of students engaged in more substantive uses that may carry academic and ethical risks. There is a need for structured AI literacy training and clear, dentistry-specific guidelines to promote responsible and transparent use while safeguarding critical thinking, academic integrity, and professional judgment in dental education.

Open educational resources (OERs) contain authentic materials that benefit students, but few studies have focused on the benefits to authors of OERs. This gap needs attention considering the challenges that OER authors face, given their commitments to multiple professional activities while also being motivated to take part in OER development. It is critical to understand what benefits authors receive, to help in the continued development of these valuable educational tools. To this end, we investigated what benefits a specific group of researcher-educators perceived from investing their limited time and energy to design, create, and share authentic OERs in the OCELOTS (Online Content for Experiential Learning of Tropical Systems) Research Coordination Network in Undergraduate Biology Education. Our study was based on conceptual frameworks for teaching and learning, communities of practice, and self-determination theory. We used qualitative data from a survey specifically designed to address the question of benefits perceived by OER authors, complemented with quantitative and qualitative data from existing internal evaluations of this network. In a content-analysis framework, we analyzed the open-ended responses to identify broader themes emerging about author benefits. OER authors reported improved pedagogical practice, increased visibility of research and outreach efforts, professional rewards, and increased collaborations. Authors reported gains in pedagogical knowledge and personal fulfillment as benefits that they received, along with satisfaction from contributing to their discipline and society in general. While benefits around improving pedagogical practice was the richest theme, creation of modules also generated new collaborations and helped strengthen and broaden authors professional networks. In particular, the sense of belonging to and building the community was a significant benefit, providing implications for how to support future OER development and the critical role of peer networks. We discuss connections across these themes and compare our results with related previous studies. These results indicate that sustained investment in intentionally designed, interdisciplinary networks can generate substantial and diverse benefits for the educators and researchers who create these resources. Open Research StatementThe de-identified data associated with this manuscript will be permanently archived in Zenodo, upon the acceptance of the manuscript.

Introduction: Trans and gender-diverse (TGD) individuals often face stigma and discrimination in healthcare, hindering access to gender-affirming care. Training healthcare workers on TGD health aims to foster inclusive and affirming care practices. This review aimed to evaluate the effectiveness of TGD health training programs for healthcare workers. Methods: This systematic review followed the PRISMA guidelines and was registered with PROSPERO (CRD42023443288). We searched 13 databases for studies up to March 2024, with no language/geographic restrictions. Ten reviewers screened studies in pairs, resolving discrepancies via discussion or third-reviewer input. We included randomized/non-randomized comparative and before-after studies for quantitative analysis (mean difference [MD] or standardized mean difference [SMD] with 95% CIs) and qualitative/mixed-methods studies for thematic synthesis. Evidence certainty was assessed using GRADE (quantitative) and GRADE-CERQual (qualitative). Outcomes included knowledge, attitudes, skills, discrimination, competence, comfort, TGD quality of life, and stakeholder preferences. Results: From 20,188 records, 85 studies were included. Training appears to have improved healthcare workers' knowledge (SMD=1.08, 95% CI 0.78-1.39), attitudes (SMD=0.22, 95% CI 0.05-0.39), skills (SMD=0.96, 95% CI 0.56-1.37), competence (SMD=0.55, 95% CI 0.29-0.81), and comfort (SMD=0.69, 95% CI 0.17-1.21). Qualitative analysis of 130 findings identified 18 categories and four key themes on intervention design and impact. Conclusions: TGD training programs may enhance health workers' knowledge, attitudes, skills, competence, and comfort. Well-structured, interactive, and inclusive programs showed promise, but evidence certainty was low with limited follow-up. Further high-quality research is needed to confirm these findings.

Participatory qualitative methods such as Photovoice are increasingly used to link research with social action. Recent advances in artificial intelligence (AI) may enhance data analysis, inference, and action planning within such participatory approaches. This study explored medical students' perceptions of social justice using conventional Photovoice analysis and assessed the potential contribution of generative AI (genAI). Nine students joined a six-week seminar, "Exploring the Concept of Social Justice Using Photovoice." An initial two-hour session covered ethics, the Photovoice framework, and photography techniques. Participants then captured images reflecting their views on social justice, wrote narratives, and engaged in guided group discussions. Human researchers and students conducted a three-stage Photovoice analysis: 1) selecting photographs, 2) contextualizing them with participant narratives, and 3) inductively coding themes. To explore how AI might support data analysis, the research team analyzed the same data with five generative tools including Sonix, ChatGPT, and Copilot. AI-generated themes and visual representations were compared with human-derived results for congruence, depth, and suggested action steps. Conventional analysis identified five major themes: (1) Social Justice and Inequality, (2) Contradictions and the Costs of Justice, (3) Community and Collective Action, (4) Environment and Environmental Justice, and (5) Perception, Subjectivity, and Perspective. AI-assisted analysis yielded six unified themes that closely aligned with human findings. Traditional Photovoice images conveyed authentic, lived experiences and strong emotional meaning, providing a powerful foundation for advocacy. AI-generated images and thematic summaries offered efficiency, creativity, and reduced researcher bias, improving generalizability. However, they lacked the emotional depth and contextual nuance present in participant-created visuals.

Introduction Creativity is important in surgery for problem-solving in the operating room and the development of surgical innovations that improve patient outcomes. However, our limited understanding of what the characteristics and competencies of the highly creative surgeon are has inhibited our ability to develop the tools, programs and interventions necessary for cultivating the creativity of surgeons. We present the protocol for the INSPIRE Study, which aims to identify the factors associated with high creative achievement in surgeons. Methods and Analysis We have designed a sequential mixed-method study, including a cohort study accompanied by qualitative semi-structured interviews. The primary objective of this study will be to identify factors associated with high creative achievement in surgeons, to be assessed through direct involvement in innovation or invention, or a top score (10 out of 10) on any domain in the Inventory of Creative Activities and Achievements questionnaire. We plan to measure 39 different personal, domain-specific, domain-general, and environmental/motivational variables, chosen based on previous literature and on exploratory grounds, to be assessed as possible factors of creative potential. Multivariable logistic regression is planned, with high creative achievement as the dependent variable and all 39 potential factors of creative potential as independent variables. Ethics and Dissemination Ethics approval from the Hamilton Integrated Research Ethics Board has been obtained and no harm is expected due to participation in this study. To facilitate knowledge translation, we plan to publish the feasibility data and results in peer-reviewed journals, and present at international surgical and creativity conferences.

INTRODUCTION In the multiple-risk approach of cardiovascular disease management, communication of cardiovascular disease risk and its prevention play a significant role. in Sri Lanka, this function is conducted via Healthy Lifestyle Centres. METHODOLOGY A clinical audit was conducted to describe communication quality in 79 healthy lifestyle centers. A checklist developed based on Patient-Centered Communication Tools with the support of an expert panel. Two trained observers independently conducted the observations while the healthcare provider at the Healthy Lifestyle Centre revealed details of cardiovascular disease risk communication and health education sessions. RESULTS: The majority of Healthy Lifestyle Centers involved patients in decision-making (n = 228, 92.0%), explained patient choices (n = 230, 92.8%) and responded to patients' interest in decision-making (n =2 35, 99.2%). Most patients received a summary (n =159,67.1%), a follow-up plan (n =212,89.5%) and were communicated in a jargon-free language (n =127,53.6%). The majority of sessions demonstrated satisfactory use of examples (95.7%, n = 22), and responsiveness to questions (73.9%, n =17). However, most sessions were unsatisfactory regarding provision of a follow-up plan (95.7%, n = 22), encouragement of questions (95.7%, n = 22), allowing clients to talk (87.0%, n = 20), and active listening (69.6%, n = 16). CONCLUSIONS: Strengthening healthcare worker training in patient-centred communication especially shared decision-making, active listening, and encouraging patient questions are essential to improve cardiovascular disease risk communication and patient adherence to preventive guidelines at Healthy Lifestyle Centres. Key words Communication, Cardiovascular disease risk, health communication, effective communication, paternalistic healthcare

Introduction: Induction of labour is a widely used obstetric intervention, yet its use varies markedly, with underuse in some settings and increasing elective use in others. However, the global prevalence and trends worldwide is unknown. We aimed to synthesise national and subnational data to estimate the prevalence of labour induction internationally and assess trends over time. Methods: We sought data from 194 countries through a structured search of national databases and relevant websites. For countries lacking adequate national data, we conducted a systematic review of published studies. Eligible data were pooled to estimate the prevalence of labour induction for 2019, and to examine temporal trends from 2010 to 2022. We used mixed-effects negative binomial regression models with missing data handled using multiple imputation by chained equations. Results: Data were obtained for 62 countries, including national-level data from 19 countries and 176 studies from 43 countries. Overall, 40 countries contributed to the 2019 estimate and 43 to the trend analysis. Most countries with data were high-income (N=37, 86.0%) and in Europe (N=29, 67.4%); there were no eligible data for sub-Saharan Africa. The estimated rate of labour induction for 2019 was 23.7% (95% confidence interval (CI): 19.3% to 29.2%). Induction had an estimated annual increase of 4% between 2010 and 2022 (incidence rate ratio 1.04, 95% CI 1.02 to 1.06). Conclusion: This study provides the first international estimates of labour induction, revealing high and rising rates globally. These trends likely reflect expanded clinical indications and improved access, but also signal potential overuse in resource-rich contexts. Our findings highlight a critical data gap in LMICs, particularly in Sub-Saharan Africa. Strengthening national perinatal data systems, especially in these settings, is essential for monitoring and guiding appropriate use. Identifying the optimal induction rate should be a priority for future research and clinical practice.

Background Sierra Leones neonatal mortality rate is among the highest in the world. Koidu Government Hospital opened a Special Care Baby Unit (SCBU) in 2020. To increase knowledge of the SCBU health care providers (HCPs), a neonatal curriculum was implemented to facilitate HCP education on management of neonatal conditions. The aim of this study was to understand the effect of the curriculum on knowledge acquisition and the perception of the teaching methodologies among participating HCPs. Methods US-based mentors facilitated a two-phase, flipped classroom, virtual neonatal medicine curriculum between October 2024 and April 2025, followed by one-week in-person education sessions with SCBU HCPs. With each phase, participants completed pre- and post-test educational assessments. At the end of the curriculum, they completed a subjective assessment to capture perceptions related to the quality of teaching methodologies integrated within the curriculum. Wilcoxon signed rank test was used to assess pre- versus post-test change. Descriptive statistics were used to analyse the subjective assessment. Results Thirty-eight participants completed the educational assessments, 30 (79%) took all four pre- and post-tests; 25/38 (65.8%) were female, 27 (71.1%) were nurses. Median correct answers for both phases increased from the pre- to post-test for individual learners [Phase 1, pre-test 14/27 (51.9%), post-test 23/27 (85.2%), p<0.001], [Phase 2, pre-test 14/25 (56.0%), post-test 23/25 (92.0%), p <0.001]. Thirty-one participants completed the subjective assessment, of whom 96.8% (30/31) rated the curriculum to be "very effective." All 31 participants indicated that the in-person instruction was "very helpful." Through open text responses, they offered valuable insight into challenges, strengths, and next steps. Conclusion This neonatal curriculum resulted in significantly increased knowledge and was well regarded. Adapting this curriculum or similar curricula show promise to improve the quality of care for small and/or sick neonates in low resource settings.

Objective This study aimed to train and evaluate supervised machine learning algorithms using electronic health record (EHR) data to accurately estimate gestational age at delivery. <br>Materials and Methods We trained random forest, gradient boosting, and ensemble models on EHR data of mother-infant dyads from Vanderbilt University Medical Center(VUMC) and replicated the analyses at University of Michigan (UMich). We further analyzed EHR predictors of gestational age, assessed temporal drift in EHR data elements, and evaluated model performance stratified by delivery status. <br>Results The study included pregnancies corresponding to 54,344 and 34,345 mother-infant dyads at VUMC (2005-2025) and UMich (2012-2024), respectively. The gestational age predictions of the ensemble models achieved the highest agreement with the reference standard on the VUMC dataset ({+/-}1 week: 85.2%, {+/-}2 weeks: 94.3%, MAE: 4.4 days) and demonstrated stronger generalization on the UMich dataset ({+/-}1 week: 93.1%, {+/-}2 weeks: 97.8%, MAE: 2.8 days). Further, performance was better among pregnancies delivered in more recent years, and among full- and late-term deliveries compared with preterm deliveries. <br>Discussion The results indicate that supervised machine learning methods leveraging linked mother-infant EHRs can accurately estimate gestational age at delivery, while demonstrating the generalizability of the modeling approach and the portability of the analytic workflow across healthcare sites. <br>Conclusion This study presents a robust and generalizable machine learning framework to estimate gestational age at delivery. The framework can be reliably used to impute gestational age in large-scale, real-world clinical studies to support maternal and neonatal health research, in which accurate estimation of pregnancy onset is critical.

ABSTRACT OBJECTIVE: We performed a systematic review and meta-analysis (SRMA) of post-surgical outcomes, comparing chlorhexidine gluconate (CHG) versus povidone iodine (PI) for vaginal antisepsis of major gynecologic procedures. DATA SOURCES: Ovid Medline, Embase, Scopus, Embase, Cochrane, and Clinicaltrials.gov were searched between 1986 and December 2023, for studies comparing CHG with PI for vaginal antisepsis of major gynecologic operations. STUDY ELIGIBILITY CRITERIA: We included Randomized Controlled Trials (RCTs) and non-RCTs comparing CHG to PI for vaginal antisepsis of major gynecologic operations. The primary outcome was surgical site infections (SSIs) and the secondary outcome was urinary tract infections (UTIs) and vaginal irritation. METHODS: Summary estimates were calculated by fixed effects models when I2 [&le;] 25% and by random effects models when I2 > 25%. Statistical analysis was performed using RevMan 5.4.1. The protocol for this systematic review was registered on PROSPERO (ID CRD42022378101). RESULTS: Nine studies met the inclusion criteria, four of which were randomized controlled trials (RCTs). 9538 patients were included, 4300 (45%) of whom were allocated to CHG and 5238 (55%) to PI. No statistically significant difference in SSI incidence was found for vaginal antisepsis with CHG versus PI in pooled analyses (n= 9538 patients; RR 1.20; 95% CI 0.92-1.57; I2 =0%). In contrast, a significantly higher risk of UTIs was observed for vaginal antisepsis with CHG than with PI (n=6061 patients; RR 1.48 95% CI 1.03-2.14; I2 = 0%). CONCLUSION: In our SRMA, there were no significant differences in SSI risk when either CHG or PI was utilized for antiseptic vaginal preparation. Interestingly, vaginal antisepsis with PI was associated with a lower incidence of post-operative UTIs following major gynecologic surgery. Our findings support current guidelines that form of vaginal antisepsis can be used for SSI prevention. They also suggest that PI may result in fewer postoperative UTIs but further randomized studies are needed to support these findings. Key words: surgical site infection, surgical wound infection, urinary tract infection, urogynecologic surgery, Chlorhexidine, Povidone Iodine, surgical antiseptic,

Understanding what is requisite for attaining a biomedical faculty career is crucial for guiding trainees preparing for these roles. For nearly two decades, we have collected accounts of biomedical training and career transitions from a large cohort through annual in-depth interviews and tracking of competencies and achievements. This paper elucidates the common and varied credentials of 40 who entered research-intensive faculty careers (RIFCs). Participants completed PhDs and postdocs in a range of research-intensive institutional settings. Developing research independence and a niche were essential to RIFC attainment, and mentors played a crucial role in this development. Counter to common assumptions, high-prestige publications and grants were not in and of themselves necessary for RIFC attainment. Our findings can aid RIFC aspirants and mentors who guide them.

Seismic shifts within academia over the last several decades have seen the growth of biomedical PhD recipients alongside the relative stagnation of tenure-track research-intensive faculty careers (RIFCs). This hypercompetitive academic job market has prompted interest in the paths of those who attain RIFCs. Understanding what drives recent biomedical PhDs to make their career decisions and persist toward them requires a clear picture of how career perceptions, motivations, and intentions develop and crystallize over time. Using annual in-depth interviews across nearly two decades, this report explores the evolution of career thinking and differentiation among 40 who attained a RIFC from diverse starting points to their attainment of a RIFC. Participants strategies for navigating early scientific experiences were patterned by their varied educational and socioeconomic backgrounds. Nearly half of participants did not start with or maintain stable interest in RIFCs, exhibiting changes in both PhD and postdoctoral phases. Participants highlighted six drivers toward RIFCs including desire for independence/autonomy and contributing to knowledge/health. Our results are instructive for trainees and mentors guiding career exploration and differentiation.